If you're shipping agents before you've stress-tested what they're actually supposed to do, you're burning compute on the wrong problem. Maurya's method tells you exactly how to find that out faster.
Running Lean by Ash Maurya is a field manual for founders who'd rather kill bad ideas early than scale them expensively. The lean canvas forces you to put your whole theory of the business on one page and then argue with it. Problem interviews come before solution demos — deliberately. The whole cadence is organized around a single discipline: identify your most dangerous assumption, the one whose failure would sink everything, and go test that one first, not the ones that feel comfortable to test.
The translation is almost uncomfortably direct. In an AI-native product, the riskiest assumption is rarely whether the market exists — it's whether your agent actually does the right thing when a real user hands it a real mess. Maurya's sequencing tells you to run problem interviews before you've built anything, which now means watching humans narrate their workflow before you've wired a single tool call. The lean canvas still works, but the "solution" box now describes agent behavior under pressure, not features on a roadmap. De-risk that box first.
- Map your lean canvas with agent behavior as the solution, not a feature list
- run problem interviews specifically to find where human judgment currently fills the gaps your agent will inherit
- treat any assumption about agent reliability as your iteration one riskiest assumption and test it before you scale the workflow.
